Dynamic Image Prediction Using Principal Component and Multi-Channel Singular Spectral Analysis: A Feasibility Study


Respiratory motion induces the limit in delivery accuracy due to the lack of the consideration of the anatomy motion in the treatment planning. Therefore, image-guided radiation therapy (IGRT) system plays an essential role in respiratory motion management and real-time tumor tracking in external beam radiation therapy. The objective of this research is the prediction of dynamic time-series images considering the motion and the deformation of the tumor and to compensate the delay that occurs between the motion of the tumor and the beam delivery. For this, we propose a prediction algorithm for dynamic time-series images. Prediction is performed using principal component analysis (PCA) and multi-channel singular spectral analysis (MSSA). Using PCA, the motion can be denoted as a vector function and it can be estimated by its principal component which is the linear combination of eigen vectors corresponding to the largest eigen values. Time-series set of 320-detector-row CT images from lung cancer patient and kilovolt (kV) fluoroscopic images from a moving phantom were used for the evaluation of the algorithm, and both image sets were successfully predicted by the proposed algorithm. The accuracy of prediction was quite high, more than 0.999 for CT images, whereas 0.995 for kV fluoroscopic images in cross-correlation coefficient value. This algorithm for image prediction makes it possible to predict the tumor images over the next breathing period with significant accuracy.

Share and Cite:

Chhatkuli, R. , Demachi, K. , Miyamoto, N. , Uesaka, M. and Haga, A. (2015) Dynamic Image Prediction Using Principal Component and Multi-Channel Singular Spectral Analysis: A Feasibility Study. Open Journal of Medical Imaging, 5, 133-142. doi: 10.4236/ojmi.2015.53017.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Cervino, L.I., Chao, A.K.Y., Sandhu, A. and Jiang, S.B. (2009) The Diaphragm as an Anatomic Surrogate for Lung Tumor Motion. Physics in Medicine and Biology, 54, 3529.
[2] Jiang, S.B. (2006) Technical Aspects of Image-Guided Respiration-Gated Radiation Therapy. Medical Dosimetry, 31, 141-151.
[3] Shirato, H., Shimizu, S., Kunieda, T., Kitamura, K., van Herk, M., Kagei, K., Nishioka, T., Hashimoto, S., Fujita, K., Aoyama, H., et al. (2000) Physical Aspects of a Real-Time Tumor-Tracking System for Gated Radiotherapy. International Journal of Radiation Oncology*Biology*Physics, 48, 1187-1195.
[4] Cho, B., Poulsen, P.R. and Keall, P.J. (2010) Real-Time Tumor Tracking Using Sequential kv Imaging Combined with Respiratory Monitoring: A General Framework Applicable to Commonly Used IGRT Systems. Physics in Medicine and Biology, 55, 3299.
[5] Ma, L. (2007) Christian Herrmann, and Klaus Schilling. Modeling and Prediction of Lung Tumor Motion for Robotic Assisted Radiotherapy. IEEE/RSJ International Conference on Intelligent Robots and Systems, 29 October 2007-2 November 2007, 189-194.
[6] Seppenwoolde, Y., Shirato, H., Kitamura, K., Shimizu, S., van Herk, M., Lebesque, J.V. and Miyasaka, K. (2002) Precise and Real-Time Measurement of 3d Tumor Motion in Lung Due to Breathing and Heartbeat, Measured during Radiotherapy. International Journal of Radiation Oncology*Biology*Physics, 53, 822-834.
[7] Isaksson, M., Jalden, J. and Murphy, M.J. (2005) On Using an Adaptive Neural Network to Predict Lung Tumor Motion during Respiration for Radiotherapy Applications. Medical Physics, 32, 3801-3809.
[8] Mukumoto, N., Nakamura, M., Yamada, M., Takahashi, K., Tanabe, H., Yano, S., Miyabe, Y., Ueki, N., Kaneko, S., Matsuo, Y., et al. (2014) Intrafractional Tracking Accuracy in Infrared Marker-Based Hybrid Dynamic Tumour- Tracking Irradiation with a Gimballed Linac. Radiotherapy and Oncology, 111, 301-305.
[9] Xu, Q., Hamilton, R.J., Schowengerdt, R.A. and Jiang, S.B. (2007) A Deformable Lung Tumor Tracking Method in Fluoroscopic Video Using Active Shape Models: A Feasibility Study. Physics in Medicine and Biology, 52, 5277.
[10] Turk, M. and Pentland, A. (1991) Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3, 71-86.
[11] Li, R., Jia, X., Lewis, J.H., Gu, X., Folkerts, M., Men, C. and Jiang, S.B. (2010) Real-Time Volumetric Image Reconstruction and 3d Tumor Localization Based on a Single X-Ray Projection Image for Lung Cancer Radiotherapy. Medical Physics, 37, 2822-2826.
[12] Mizuguchi, A., Demachi, K. and Uesaka, M. (2010) Establish of the Prediction System of Chest Skin Motion with SSA Method. International Journal of Applied Electromagnetics and Mechanics, 33, 1529-1533.
[13] Ouzidane, M., Evans, J. and Djemil, T. (2014) Dedicated Linear Accelerators for Stereotactic Radiation Therapy. In: Benedict, S.H., Schlesinger, D.J., Goetsch, S.J. and Kavanagh, B.D., Eds., Sterotactic Radiosurgery and Stereotactic Body Radiation Therapy, CRC Press, Taylor and Francis Group, Boca Raton, 33487-2742.
[14] Jia, X., Ziegenhein, P. and Jiang, S.B. (2014) GPU-Based High-Performance Computing for Radiation Therapy. Physics in Medicine and Biology, 59, R151.

Copyright © 2021 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.